Skip to content

ADR-0006: Data Persistence Strategy

Status

PROPOSED

Date: 2025-07-05

Changelog

Date Revision Description Author
2025-07-05 1.0 Initial version souperk

Context

The Loryth project requires a robust data persistence strategy to handle various data storage needs across services. Key considerations include:

  • Each service has different data access patterns and requirements
  • Need for performance optimization for frequently accessed data
  • Database connection management for optimal resource utilization
  • Data consistency across distributed services
  • Audit requirements for data changes
  • Scalability as data volume grows
  • Data security and access control

Without a well-defined persistence strategy, we risk performance bottlenecks, inconsistent implementation across services, and difficulty scaling as the system grows.

Decision

We will implement a multi-faceted data persistence strategy with the following components:

  1. Database Selection:

    • Primary relational database (Postgres) for transactional data
    • Service-specific databases to maintain service independence
    • Potential specialized stores for specific needs (e.g., document store for CMS service)

  2. Database Optimization:

    • Proper indexing strategy based on query patterns
    • Regular query performance analysis and optimization
    • Database partitioning for large tables
    • Read replicas for read-heavy services

  3. Caching Strategy:

    • Multi-level caching approach:
      • Application-level caching for computed results
      • Distributed cache (Redis) for sharing data across instances
      • Database query cache for repetitive queries
    • Cache invalidation strategies:
      • Time-based expiration for less critical data
      • Event-based invalidation for consistency-critical data
      • Version-tagged cache entries

  4. Connection Management:

    • Connection pooling configuration optimized per service
    • Monitoring of connection usage and pool sizing
    • Circuit breaker pattern for database resilience
    • Read/write splitting where appropriate

  5. Data Access Patterns:

    • Repository pattern for data access abstraction
    • CQRS (Command Query Responsibility Segregation) for complex domains
    • Optimistic concurrency control for collaborative editing

  6. Audit and Compliance:

    • Comprehensive audit logging for all data changes
    • Temporal data patterns for historical tracking
    • Data retention policies aligned with requirements

Consequences

Positive

  • Performance optimization: Targeted database optimization improves response times
  • Scalability: Multiple strategies allow scaling different components independently
  • Resilience: Connection pooling and circuit breakers improve system stability
  • Flexibility: Service-specific persistence allows tailored solutions
  • Auditability: Comprehensive audit logging enables compliance and troubleshooting

Negative

  • Increased complexity: Multiple databases and caching layers add operational complexity
  • Consistency challenges: Distributed data and caching require careful consistency management
  • Learning curve: Team needs to understand various persistence technologies
  • Operational overhead: More components to monitor and maintain

Neutral

  • Technology diversity: Different storage technologies require broader expertise
  • Migration complexity: Future schema changes need careful coordination
  • Monitoring requirements: Comprehensive monitoring needed across all persistence layers

Alternatives Considered

  1. Shared Database Approach

    • Description: Single database shared across all services
    • Pros:
      • Simplified data consistency
      • Easier transactions across services
      • Reduced operational complexity
    • Cons:
      • Tight coupling between services
      • Limited independent scaling
      • Schema changes affect multiple services
    • Why not chosen: Would undermine service independence and scalability

  2. Polyglot Persistence for All Services

    • Description: Different database technology for each service based on specific needs
    • Pros:
      • Optimal technology for each service's needs
      • Maximum flexibility in data storage
    • Cons:
      • Significant operational complexity
      • Diverse expertise required
      • Challenging data integration
    • Why not chosen: Added complexity without clear benefit for all services

  3. NoSQL-Only Approach

    • Description: Use NoSQL databases exclusively across all services
    • Pros:
      • Potentially easier horizontal scaling
      • Schema flexibility
      • Potentially simpler development for some use cases
    • Cons:
      • Less mature tooling for some operations
      • Transaction limitations
      • Not optimal for all data patterns
    • Why not chosen: Not all services benefit from NoSQL characteristics

References